Inherent Virality
Nov 4, 2024

In all my ventures—whether building site builders, classified ads platforms, social media tools, or games—one critical lesson has consistently emerged: integrating distribution into the product from day one is essential. It's not enough to create a great product; the way it spreads and reaches new users must be woven into its very fabric.

One of the most potent strategies for achieving this is through inherent virality. This concept revolves around designing a product that naturally encourages users to share it as part of their experience. The product's value isn't fully realized until it's shared with others, and the act of sharing enhances its utility for the user.

When a product possesses inherent virality, every satisfied user becomes a potential advocate, introducing the product to new users who are likely to find similar value. This creates a self-sustaining loop where focusing on delighting one user effectively means satisfying many.

Consider offline businesses like hair salons or wedding planners. A single exceptional wedding planned doesn't just please the bride and groom; it impresses the guests, many of whom may be planning their own events. Their satisfaction leads them to seek out the same service, organically expanding the business's reach.

In the digital realm, tools like Zoom and Calendly epitomize inherent virality. When you use Zoom to host a meeting, you send a link to participants who experience the platform firsthand. If they find it valuable, they're likely to use it for their meetings. Calendly works similarly; scheduling with someone introduces them to the platform, potentially converting them into users.

These products thrive because the very act of using them involves sharing with others, making distribution a natural outcome of engagement. However, building such products is challenging. Users are essentially endorsing your product to their network, so the quality must be exceptional. They're putting their reputation on the line, and any misstep can erode trust.

Let’s explore another project that I'm working on: the creation of a "Google Maps of Work" powered by digital twins and AI. This platform embodies inherent virality in the realm of AI and collaboration.

Traditional professional networks and platforms focus heavily on the past—resumes, past projects, published papers, and completed work. While this information is valuable, it doesn't necessarily facilitate collaboration or innovation in the present or future. What truly propels progress is understanding where people want to go, the problems they're eager to solve, and their future goals.

The "Google Maps of Work" shifts the focus from past accomplishments to future aspirations. By creating digital twins—AI-powered representations of individuals and companies—we can map out the goals, interests, and challenges that professionals are currently engaging with or wish to tackle. This dynamic map allows for real-time collaboration opportunities and connections that were previously difficult to identify.

Imagine a platform where you input the problems you're passionate about solving. An AI project manager analyzes your interests and connects you with others who have complementary goals. It can answer questions about your work, suggest potential collaborators, and even facilitate introductions. This creates a network effect rooted in future possibilities rather than past achievements.

The inherent virality comes into play because users are motivated to share the platform with their network. If you're seeking collaborators or assistance on a project, you're likely to invite others to join so they can interact with your digital twin. Similarly, those interested in your work can easily engage with you through the platform, promoting further sharing and engagement.

With the advent of LLMs, mapping the complex world of work becomes feasible. These technologies can understand and process vast amounts of information about individual goals, industry challenges, and potential synergies. By creating digital twins for companies and individuals—whether solo entrepreneurs or multinational corporations—we can visualize the problems being tackled across the globe.

A recent study published in Nature Human Behaviour titled "Accelerating science with human-aware artificial intelligence" highlights a method that aligns perfectly with this vision. The researchers demonstrated that by integrating human cognitive dynamics into AI models, we can significantly enhance the prediction of scientific discoveries. They achieved this by simulating the pathways scientists take through publications and collaboration networks, effectively mapping out how human expertise interacts with content.

Applying this method to the "Google Maps of Work," we can create a hypergraph that represents not just scientific knowledge but the entire world of work. By simulating the way professionals navigate their networks—through collaborations, shared interests, and mutual goals—we can enhance our AI models to predict and facilitate valuable connections. This human-aware AI approach doesn't just forecast probable collaborations; it also uncovers "hidden" opportunities that might be overlooked by traditional methods.

By integrating these techniques, our platform can accelerate innovation across all industries, not just research. It can help identify gaps where expertise is lacking, suggest potential partnerships, and even propose novel solutions to complex problems. This approach ensures that the AI is not operating in a vacuum but is deeply attuned to human dynamics and the real-world context of work.

The future of work is not just about what we've done—it's about what we're doing and where we're headed. By harnessing human-aware AI, we can map this journey more effectively, ensuring that opportunities for collaboration and innovation are accessible to all. Let's build the tools that get us there together.